OpenRaiser/Intern-Atlas huggingface dataset:
Patch Refinement -- Localized 3D Object Detection
Venue: arXiv cs.CV 2019
Year: 2019

We introduce Patch Refinement a two-stage model for accurate 3D object detection and localization from point cloud data. Patch Refinement is composed of two independently trained Voxelnet-based networks, a Region Proposal Network (RPN) and a Local Refinement Network (LRN). We decompose the detection task into a preliminary Bird's Eye View (BEV) detection step and a local 3D detection step. Based on the proposed BEV locations by the RPN, we extract small point cloud subsets ("patches"), which are then processed by the LRN, which is less limited by memory constraints due to the small area of each patch. Therefore, we can apply encoding with a higher voxel resolution locally. The independence of the LRN enables the use of additional augmentation techniques and allows for an efficient, regression focused training as it uses only a small fraction of each scene. Evaluated on the KITTI 3D object detection benchmark, our submission from January 28, 2019, outperformed all previous entries on all three difficulties of the class car, using only 50 % of the available training data and only LiDAR information.
Unaligned Image-to-Sequence Transformation with Loop Consistency
Venue: arXiv cs.CV 2019
Year: 2019

We tackle the problem of modeling sequential visual phenomena. Given examples of a phenomena that can be divided into discrete time steps, we aim to take an input from any such time and realize this input at all other time steps in the sequence. Furthermore, we aim to do this without ground-truth aligned sequences -- avoiding the difficulties needed for gathering aligned data. This generalizes the unpaired image-to-image problem from generating pairs to generating sequences. We extend cycle consistency to loop consistency and alleviate difficulties associated with learning in the resulting long chains of computation. We show competitive results compared to existing image-to-image techniques when modeling several different data sets including the Earth's seasons and aging of human faces.
Efficient Semi-Supervised Learning for Natural Language Understanding by Optimizing Diversity
Venue: arXiv cs.CL 2019
Year: 2019

Expanding new functionalities efficiently is an ongoing challenge for single-turn task-oriented dialogue systems. In this work, we explore functionality-specific semi-supervised learning via self-training. We consider methods that augment training data automatically from unlabeled data sets in a functionality-targeted manner. In addition, we examine multiple techniques for efficient selection of augmented utterances to reduce training time and increase diversity. First, we consider paraphrase detection methods that attempt to find utterance variants of labeled training data with good coverage. Second, we explore sub-modular optimization based on n-grams features for utterance selection. Experiments show that functionality-specific self-training is very effective for improving system performance. In addition, methods optimizing diversity can reduce training data in many cases to 50% with little impact on performance.
A New Cryptosystem Based on Positive Braids
Venue: arXiv cs.CR 2019
Year: 2019

The braid group is an important non commutative group, at the same time, it is an important tool in quantum field theory with better topological structure, and often used as a research carrier for anti-quantum cryptographic algorithms. This paper proposed a difficult problem on a positive braid semi-group, and proved that the difficulty is not lower than the conjugate search problem. Based on this new difficult problem, we propose a new cryptosystem, which include a key exchange protocol and a public key encryption algorithm. Since our cryptosystem is implemented on a semi-group, it effectively avoids the analysis of attack algorithms on the cluster and makes our algorithm more secure.
Active Learning with Importance Sampling
Venue: arXiv cs.LG 2019
Year: 2019

We consider an active learning setting where the algorithm has access to a large pool of unlabeled data and a small pool of labeled data. In each iteration, the algorithm chooses few unlabeled data points and obtains their labels from an oracle. In this paper, we consider a probabilistic querying procedure to choose the points to be labeled. We propose an algorithm for Active Learning with Importance Sampling (ALIS), and derive upper bounds on the true loss incurred by the algorithm for any arbitrary probabilistic sampling procedure. Further, we propose an optimal sampling distribution that minimizes the upper bound on the true loss.
Controllable Sentence Simplification: Employing Syntactic and Lexical Constraints
Venue: arXiv cs.CL 2019
Year: 2019

Sentence simplification aims to make sentences easier to read and understand. Recent approaches have shown promising results with sequence-to-sequence models which have been developed assuming homogeneous target audiences. In this paper we argue that different users have different simplification needs (e.g. dyslexics vs. non-native speakers), and propose CROSS, ContROllable Sentence Simplification model, which allows to control both the level of simplicity and the type of the simplification. We achieve this by enriching a Transformer-based architecture with syntactic and lexical constraints (which can be set or learned from data). Empirical results on two benchmark datasets show that constraints are key to successful simplification, offering flexible generation output.
Hierarchical Representation Network for Steganalysis of QIM Steganography in Low-Bit-Rate Speech Signals
Venue: arXiv cs.MM 2019
Year: 2019

With the Volume of Voice over IP (VoIP) traffic rises shapely, more and more VoIP-based steganography methods have emerged in recent years, which poses a great threat to the security of cyberspace. Low bit-rate speech codecs are widely used in the VoIP application due to its powerful compression capability. QIM steganography makes it possible to hide secret information in VoIP streams. Previous research mostly focus on capturing the inter-frame correlation or inner-frame correlation features in code-words but ignore the hierarchical structure which exists in speech frame. In this paper, motivated by the complex multi-scale structure, we design a Hierarchical Representation Network to tackle the steganalysis of QIM steganography in low-bit-rate speech signal. In the proposed model, Convolution Neural Network (CNN) is used to model the hierarchical structure in the speech frame, and three level of attention mechanisms are applied at different convolution block, enabling it to attend differentially to more and less important content in speech frame. Experiments demonstrated that the steganalysis performance of the proposed method can outperforms the state-of-the-art methods especially in detecting both short and low embeded speech samples. Moreover, our model needs less computation and has higher time efficiency to be applied to real online services.
Semi-Supervised Variational Autoencoder for Survival Prediction
Venue: arXiv cs.CV 2019
Year: 2019

In this paper we propose a semi-supervised variational autoencoder for classification of overall survival groups from tumor segmentation masks. The model can use the output of any tumor segmentation algorithm, removing all assumptions on the scanning platform and the specific type of pulse sequences used, thereby increasing its generalization properties. Due to its semi-supervised nature, the method can learn to classify survival time by using a relatively small number of labeled subjects. We validate our model on the publicly available dataset from the Multimodal Brain Tumor Segmentation Challenge (BraTS) 2019.
Strategic Coalitions in Stochastic Games
Venue: arXiv cs.AI 2019
Year: 2019

The article introduces a notion of a stochastic game with failure states and proposes two logical systems with modality "coalition has a strategy to transition to a non-failure state with a given probability while achieving a given goal." The logical properties of this modality depend on whether the modal language allows the empty coalition. The main technical results are a completeness theorem for a logical system with the empty coalition, a strong completeness theorem for the logical system without the empty coalition, and an incompleteness theorem which shows that there is no strongly complete logical system in the language with the empty coalition.
The Quest for Interpretable and Responsible Artificial Intelligence
Venue: arXiv cs.AI 2019
Year: 2019

Artificial Intelligence (AI) provides many opportunities to improve private and public life. Discovering patterns and structures in large troves of data in an automated manner is a core component of data science, and currently drives applications in computational biology, finance, law and robotics. However, such a highly positive impact is coupled with significant challenges: How do we understand the decisions suggested by these systems in order that we can trust them? How can they be held accountable for those decisions? In this short survey, we cover some of the motivations and trends in the area that attempt to address such questions.
QPyTorch: A Low-Precision Arithmetic Simulation Framework
Venue: arXiv cs.LG 2019
Year: 2019

Low-precision training reduces computational cost and produces efficient models. Recent research in developing new low-precision training algorithms often relies on simulation to empirically evaluate the statistical effects of quantization while avoiding the substantial overhead of building specific hardware. To support this empirical research, we introduce QPyTorch, a low-precision arithmetic simulation framework. Built natively in PyTorch, QPyTorch provides a convenient interface that minimizes the efforts needed to reliably convert existing codes to study low-precision training. QPyTorch is general, and supports a variety of combinations of precisions, number formats, and rounding options. Additionally, it leverages an efficient fused-kernel approach to reduce simulator overhead, which enables simulation of large-scale, realistic problems. QPyTorch is publicly available at https://github.com/Tiiiger/QPyTorch.
Security analysis of a blockchain-based protocol for the certification of academic credentials
Venue: arXiv cs.CR 2019
Year: 2019

We consider a blockchain-based protocol for the certification of academic credentials named Blockcerts, which is currently used worldwide for validating digital certificates of competence compliant with the Open Badges standard. We study the certification steps that are performed by the Blockcerts protocol to validate a certificate, and find that they are vulnerable to a certain type of impersonation attacks. More in detail, authentication of the issuing institution is performed by retrieving an unauthenticated issuer profile online, and comparing some data reported there with those included in the issued certificate. We show that, by fabricating a fake issuer profile and generating a suitably altered certificate, an attacker is able to impersonate a legitimate issuer and can produce certificates that cannot be distinguished from originals by the Blockcerts validation procedure. We also propose some possible countermeasures against an attack of this type, which require the use of a classic public key infrastructure or a decentralized identity system integrated with the Blockcerts protocol.
Comparison of Generative Adversarial Networks Architectures Which Reduce Mode Collapse
Venue: arXiv cs.LG 2019
Year: 2019

Generative Adversarial Networks are known for their high quality outputs and versatility. However, they also suffer the mode collapse in their output data distribution. There have been many efforts to revamp GANs model and reduce mode collapse. This paper focuses on two of these models, PacGAN and VEEGAN. This paper explains the mathematical theory behind aforementioned models, and compare their degree of mode collapse with vanilla GAN using MNIST digits as input data. The result indicates that PacGAN performs slightly better than vanilla GAN in terms of mode collapse, and VEEGAN performs worse than both PacGAN and vanilla GAN. VEEGAN's poor performance may be attributed to average autoencoder loss in its objective function and small penalty for blurry features.
GLADAS: Gesture Learning for Advanced Driver Assistance Systems
Venue: arXiv cs.HC 2019
Year: 2019

Human-computer interaction (HCI) is crucial for the safety of lives as autonomous vehicles (AVs) become commonplace. Yet, little effort has been put toward ensuring that AVs understand humans on the road. In this paper, we present GLADAS, a simulator-based research platform designed to teach AVs to understand pedestrian hand gestures. GLADAS supports the training, testing, and validation of deep learning-based self-driving car gesture recognition systems. We focus on gestures as they are a primordial (i.e, natural and common) way to interact with cars. To the best of our knowledge, GLADAS is the first system of its kind designed to provide an infrastructure for further research into human-AV interaction. We also develop a hand gesture recognition algorithm for self-driving cars, using GLADAS to evaluate its performance. Our results show that an AV understands human gestures 85.91% of the time, reinforcing the need for further research into human-AV interaction.
Immersive virtual worlds: Multi-sensory virtual environments for health and safety training
Venue: arXiv cs.HC 2019
Year: 2019

Virtual environments (VEs) offer potential benefits to health and safety training: exposure to dangerous (virtual) environments; the opportunity for experiential learning; and a high level of control over the training, in that aspects can be repeated or reviewed based on the trainee's performance. However, VEs are typically presented as audiovisual (AV) systems, whereas engagement of other senses could increase the immersion in the virtual experience. Moreover, other senses play a key role in certain health and safety contexts, for example the feel of heat and smell in a fire or smell in a fuel leak. A multisensory (MS) VE was developed, which provided simulated heat and smell in accordance with events in a virtual world. As users approached a virtual fire, they felt heat from three 2 kW heaters and smelled smoke from a scent diffuser. Behaviours in the MS VE demonstrated higher validity than those in a comparable AV VE, which ratings and verbatim responses indicated was down to a greater belief that participants were in a real fire. However, a study of the effectiveness of the MS VE as a training tool demonstrated that it did not offer benefits over AV as measured by a written knowledge test and subjective ratings of engagement, attitude towards health and safety and desire to repeat. However, the study found further evidence for the use of AV VEs in health and safety training, particularly as the subjective ratings were generally better than for PowerPoint based training. Despite the lack of evidence for MS simulation on traditional measures of training, the different attitudes and experiences of users suggest that it may have value as a system for changing trainees' attitudes towards their personal safety and awareness. This view was supported by feedback from industrial partners.
NEURO-DRAM: a 3D recurrent visual attention model for interpretable neuroimaging classification
Venue: arXiv cs.LG 2019
Year: 2019

Deep learning is attracting significant interest in the neuroimaging community as a means to diagnose psychiatric and neurological disorders from structural magnetic resonance images. However, there is a tendency amongst researchers to adopt architectures optimized for traditional computer vision tasks, rather than design networks customized for neuroimaging data. We address this by introducing NEURO-DRAM, a 3D recurrent visual attention model tailored for neuroimaging classification. The model comprises an agent which, trained by reinforcement learning, learns to navigate through volumetric images, selectively attending to the most informative regions for a given task. When applied to Alzheimer's disease prediction, NEURODRAM achieves state-of-the-art classification accuracy on an out-of-sample dataset, significantly outperforming a baseline convolutional neural network. When further applied to the task of predicting which patients with mild cognitive impairment will be diagnosed with Alzheimer's disease within two years, the model achieves state-of-the-art accuracy with no additional training. Encouragingly, the agent learns, without explicit instruction, a search policy in agreement with standardized radiological hallmarks of Alzheimer's disease, suggesting a route to automated biomarker discovery for more poorly understood disorders.
Expression, Affect, Action Unit Recognition: Aff-Wild2, Multi-Task Learning and ArcFace
Venue: arXiv cs.CV 2019
Year: 2019

Affective computing has been largely limited in terms of available data resources. The need to collect and annotate diverse in-the-wild datasets has become apparent with the rise of deep learning models, as the default approach to address any computer vision task. Some in-the-wild databases have been recently proposed. However: i) their size is small, ii) they are not audiovisual, iii) only a small part is manually annotated, iv) they contain a small number of subjects, or v) they are not annotated for all main behavior tasks (valence-arousal estimation, action unit detection and basic expression classification). To address these, we substantially extend the largest available in-the-wild database (Aff-Wild) to study continuous emotions such as valence and arousal. Furthermore, we annotate parts of the database with basic expressions and action units. As a consequence, for the first time, this allows the joint study of all three types of behavior states. We call this database Aff-Wild2. We conduct extensive experiments with CNN and CNN-RNN architectures that use visual and audio modalities; these networks are trained on Aff-Wild2 and their performance is then evaluated on 10 publicly available emotion databases. We show that the networks achieve state-of-the-art performance for the emotion recognition tasks. Additionally, we adapt the ArcFace loss function in the emotion recognition context and use it for training two new networks on Aff-Wild2 and then re-train them in a variety of diverse expression recognition databases. The networks are shown to improve the existing state-of-the-art. The database, emotion recognition models and source code are available at http://ibug.doc.ic.ac.uk/resources/aff-wild2.
Video-Based Convolutional Attention for Person Re-Identification
Venue: arXiv cs.CV 2019
Year: 2019

In this paper we consider the problem of video-based person re-identification, which is the task of associating videos of the same person captured by different and non-overlapping cameras. We propose a Siamese framework in which video frames of the person to re-identify and of the candidate one are processed by two identical networks which produce a similarity score. We introduce an attention mechanisms to capture the relevant information both at frame level (spatial information) and at video level (temporal information given by the importance of a specific frame within the sequence). One of the novelties of our approach is given by a joint concurrent processing of both frame and video levels, providing in such a way a very simple architecture. Despite this fact, our approach achieves better performance than the state-of-the-art on the challenging iLIDS-VID dataset.
SoK: Hardware Security Support for Trustworthy Execution
Venue: arXiv cs.CR 2019
Year: 2019

In recent years, there have emerged many new hardware mechanisms for improving the security of our computer systems. Hardware offers many advantages over pure software approaches: immutability of mechanisms to software attacks, better execution and power efficiency and a smaller interface allowing it to better maintain secrets. This has given birth to a plethora of hardware mechanisms providing trusted execution environments (TEEs), support for integrity checking and memory safety and widespread uses of hardware roots of trust. In this paper, we systematize these approaches through the lens of abstraction. Abstraction is key to computing systems, and the interface between hardware and software contains many abstractions. We find that these abstractions, when poorly designed, can both obscure information that is needed for security enforcement, as well as reveal information that needs to be kept secret, leading to vulnerabilities. We summarize such vulnerabilities and discuss several research trends of this area.
VarGFaceNet: An Efficient Variable Group Convolutional Neural Network for Lightweight Face Recognition
Venue: arXiv cs.CV 2019
Year: 2019

To improve the discriminative and generalization ability of lightweight network for face recognition, we propose an efficient variable group convolutional network called VarGFaceNet. Variable group convolution is introduced by VarGNet to solve the conflict between small computational cost and the unbalance of computational intensity inside a block. We employ variable group convolution to design our network which can support large scale face identification while reduce computational cost and parameters. Specifically, we use a head setting to reserve essential information at the start of the network and propose a particular embedding setting to reduce parameters of fully-connected layer for embedding. To enhance interpretation ability, we employ an equivalence of angular distillation loss to guide our lightweight network and we apply recursive knowledge distillation to relieve the discrepancy between the teacher model and the student model. The champion of deepglint-light track of LFR (2019) challenge demonstrates the effectiveness of our model and approach. Implementation of VarGFaceNet will be released at https://github.com/zma-c-137/VarGFaceNet soon.
Generalized Planning With Procedural Domain Control Knowledge
Venue: arXiv cs.AI 2019
Year: 2019

Generalized planning is the task of generating a single solution that is valid for a set of planning problems. In this paper we show how to represent and compute generalized plans using procedural Domain Control Knowledge (DCK). We define a {\it divide and conquer} approach that first generates the procedural DCK solving a set of planning problems representative of certain subtasks and then compile it as callable procedures of the overall generalized planning problem. Our procedure calling mechanism allows nested and recursive procedure calls and is implemented in PDDL so that classical planners can compute and exploit procedural DCK. Experiments show that an off-the-shelf classical planner, using procedural DCK as callable procedures, can compute generalized plans in a wide range of domains including non-trivial ones, such as sorting variable-size lists or DFS traversal of binary trees with variable size.
Automatic segmentation of texts into units of meaning for reading assistance
Venue: arXiv cs.CL 2019
Year: 2019

The emergence of the digital book is a major step forward in providing access to reading, and therefore often to the common culture and the labour market. By allowing the enrichment of texts with cognitive crutches, EPub 3 compatible accessibility formats such as FROG have proven their effectiveness in alleviating but also reducing dyslexic disorders. In this paper, we show how Artificial Intelligence and particularly Transfer Learning with Google BERT can automate the division into units of meaning, and thus facilitate the creation of enriched digital books at a moderate cost.